Comments Off on Artificial intelligence will dominate every aspect of our lives, but it won’t replace us

Guess which of the 964 jobs listed in the widely used Occupational Information Network online database is the least susceptible to replacement by artificial intelligence (AI).

The unsurprising answer is that of “massage therapist”.

This is one of the findings of a paper in the latest issue of the American Economic Review by Erik Brynjolfsson and colleagues at MIT’s Sloan School of Management.

But, while this answer might seem obvious, the study itself is a serious and innovative attempt to analyse the potential impact of AI on occupations across the economy.

A key point is that AI technology itself is going through a period of revolutionary progress.

The success of Google’s Deep Mind team in defeating the world champion at the immensely complex game of Go received wide publicity.

Unlike the algorithms which vanquished chess some years previously, the latest AlphaGo programme – improved since its annihilation of the Go champion less than two years ago – does not simply rely on pure computing power to outperform humans. The algorithm starts by knowing absolutely nothing about the game. It becomes stronger by playing against itself and learning as it goes along.

In short, it teaches itself, remembering both its mistakes and its successes. This type of algorithm is very new, and is known as deep learning. The programmes automatically improve their performance at a task through experience.

Brynjolfsson and colleagues regard this as so significant that they describe deep learning as a “general purpose technology” (GPT).

GPTs are technologies which become pervasive throughout the economy, improve over time, and generate further innovations which are complementary.

Historically, they are few and far between. Steam and electricity are examples. If they disappeared tomorrow, we would rapidly be driven back to the living standard which existed several centuries ago.

Deep learning will take years – or even several decades – before anything like its full effects are realised. But we will then look back and find that it is just as hard to imagine a world without deep learning as it is a world without electricity.

What will that look like? The authors analyse 2,069 work activities and 18,156 tasks in the 964 occupations. From this, they build “suitability for machine learning” (SML) measures for labour inputs in the US economy. They find that most occupations in most industries have at least some tasks that are SML. Pretty obvious. But few, if any, occupations have all tasks that are SML.

This latter point certainly is surprising – and from it the MIT team derives a positive message: very few jobs can be fully automated using this new technology.

A fundamental shift is needed in the debate about the effects of AI on work. Instead of the common concerns about the full automation of many jobs and pervasive occupational replacement, we should be thinking about the redesign of jobs and reengineering of business processes.

Economics is often described as the dismal science. But Brynjolfsson’s paper certainly provides very positive food for thought.

Last week we saw yet another major reversal of opinion by experts. For years we have all been lectured severely on the need to finish every single course of prescription drugs.

But the latest wisdom is that this is not necessary.

The announcement that petrol and diesel cars will be banned by 2040 only serves to remind the millions of diesel car owners that they were told only a few years ago that diesel was a Good Thing.

These stories have been very prominent in the media. But they are by no means isolated examples. Such reversals of opinion are all too common in the softer social and medical sciences. The “evidence base”, a phrase beloved of metropolitan liberal experts, is often built on sand.

This is neatly illustrated by psychology. Science is probably the most prestigious scientific journal in the world. At the end of 2015, a group of no fewer than 270 authors published a paper in it. They were all part of the teams which had published 100 scientific articles in top psychology journals.

In only 16 out of the 100 cases could the experimental results be replicated sufficiently closely to be confident that the original finding was valid.

The papers had been published in top psychology journals, and the original authors were involved in the replication experiment. So the replication rate should have been high.

Instead, it was so low that the lead author of the Science piece points out that they effectively knew nothing. The original finding could be correct, the different result in the attempted replication could be. Or neither of these could be true.

There is no suggestion at all that any sort of fraud or misrepresentation was involved when the original results were submitted for publication. But economic theory helps us understand how this absurd situation came about.

The great insight of economics is that people react to incentives.

Academics now face immense pressure to publish research papers. If they do not, they get more burdensome teaching loads, miss out on promotions, and might even get sacked. Their incentive is to publish.

Academic journals will only very rarely accept a paper which contains negative results. The whole culture is to find positive ones. So experiments will be re-designed, run with different samples, until that sought-after positive finding is obtained.

More and more academics are now desperate to publish more and more research papers. To meet this increase in demand, there has been a massive increase in the supply of journals willing to publish. Many of these are highly dubious, prepared to accept papers on payment of a fee by the authors.

For all except a small elite of individuals and institutions, academic life has become increasingly proletarianised. In the old Soviet Union, workers could get medals for exceeding the quota of, say, boot production. It did not matter if all the boots were left footed.

Many universities are now similar, with useless articles being churned out to meet the demands of bureaucrats. Time for a big purge, both of academics and their institutions.